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1,049 results for “robustness”
Data from: Coordination of wing and whole body development at developmental milestones ensures robustness against environmental and physiological perturbations
Development produces correctly patterned tissues under a wide range of conditions that alter the rate of development in the whole body. We propose two hypotheses through which tissue patterning could be coordinated with whole body development to generate this robustness. Our first hypothesis states that tissue patterning is tightly coordinated with whole body development over time. The second hypothesis is that tissue patterning aligns at developmental milestones. To distinguish between our two hypotheses, we developed a staging scheme for the wing imaginal discs of Drosophila larvae using the expression of canonical patterning genes, linking our scheme to three whole body developmental events, moulting, larval wandering and pupariation. We used our scheme to explore how the progression of pattern changes when developmental time is altered either by changing temperature or by altering the timing of hormone synthesis that drives developmental progression. We found the expression pattern in the wing disc always aligned at moulting and pupariation, indicating that these key developmental events represent milestones. Between these milestones, the progression of pattern showed greater variability in response to changes in temperature and alterations in physiology. Furthermore, our data showed that discs from wandering larvae had greater variability in their patterning stage. Thus, for wing disc patterning wandering does not appear to be a developmental milestone. Our findings reveal that tissue patterning remains robust against environmental and physiological perturbations by aligning at developmental milestones. Furthermore, our work provides an important glimpse into how the development of individual tissues is coordinated with the body as a whole.
Computational results for the work "Gradient-robust hybrid DG discretizations for the compressible Stokes equations"
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Robust Active Measuring under Model Uncertainty - Code
<p>Repository containing code, as well as gathered data, as used for the paper</p> <blockquote> <p>Merlijn Krale, Thiago D. Simao, Jana Tumova, Nils Jansen<br>Robust Active Measuring under Model Uncertainty<br>In AAAI, 2024.</p> </blockquote> <p>For instructions, see the readme.md file in the repository.</p> <p>All code can also be found on GitHub, at <a href="https://github.com/LAVA-LAB/RATM">https://github.com/LAVA-LAB/RATM</a>.</p>
k-space dataset for Robust multishot diffusion-weighted imaging of the abdomen with region-based shot rejection
<p>k-space dataset for https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.30102</p>
Rational Design of 7-Azaindole-Based Robust Microporous Hydrogen-Bonded Organic Framework for Gas Sorption
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Dataset: Modification of Gradient HPLC Method for Determination of Small Molecules' Affinity to Human Serum Albumin under Column Safety Conditions: Robustness and Chemometrics Study
<p>Dataset for publication.</p> <p> </p>
Revisiting the Relation Between Robustness and Universality: Part 2
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Revisiting the Relation Between Robustness and Universality: Part 4
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An Approach to Assess Robustness of MQTT-based IoT Systems - Supplementary Material
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Evaluation results from "Next2You: Robust Copresence Detection Based on Channel State Information"
<p>This deposit contains the evaluation results for the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick in ACM Transactions on Internet of Things, Volume 3, Issue 2. 2022. See the <a href="https://doi.org/10.5281/zenodo.5105815">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Raw data from "Next2You: Robust Copresence Detection Based on Channel State Information"
<p>This deposit contains the raw channel state information (CSI) data from the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick in ACM Transactions on Internet of Things, Volume 3, Issue 2. 2022. See the <a href="https://doi.org/10.5281/zenodo.5105815">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Index of Supplementary Files from "Next2You: Robust Copresence Detection Based on Channel State Information"
<p>This record serves as an index to the other dataset releases that are part of the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick in ACM Transactions on Internet of Things, Volume 3, Issue 2. 2022. </p> <p>We have chosen to split the dataset into several parts to meet Zenodo size requirements and make it easier to find specific pieces of data. In total, the following datasets exist:</p> <ol> <li> <p><strong>Raw data</strong>: <a href="https://doi.org/10.5281/zenodo.5592335">This dataset</a> contains raw channel state information (CSI) data in terms of CSI magnitude and phase values. The data is collected in the following environments: <em>office</em>, <em>urban apartment</em>, <em>rural house</em>, <em>moving</em> and <em>parked</em> cars, as well as additional setups in these environments such as <em>heterogeneous</em> devices (i.e., Nexus 6P and Raspberry Pi), different <em>frame types</em> to extract CSI data from (i.e., beacon), and varying transmission <em>power</em> of devices sending frames from which CSI is extracted. The data collection in additional setups was performed in the office environment.</p> <p>These raw CSI data can be used to repeat our own experiment or to develop new context-based copresence detection schemes. To collect the raw CSI data, we used the following <a href="https://github.com/seemoo-lab/next2you/tree/main/Data-collection/Nexus%205%20and%206P">Android app</a> and <a href="https://github.com/seemoo-lab/next2you/tree/main/Data-collection/Raspberry%20Pi">scripts on the Raspberry Pi 3 Model B+</a> (both include <a href="https://github.com/seemoo-lab/nexmon_csi">Nexmon</a> patches to enable CSI extraction).</p> </li> <li> <p><strong>Results Data</strong>: <a href="https://doi.org/10.5281/zenodo.5592823">The results dataset</a> contains the evaluation results (e.g., computed error rates and AUCs for different cases, Right for the Right Reasons trained models, etc.). The codebase to generate these results can be found in the <a href="https://github.com/seemoo-lab/next2you/tree/main/Scheme">source code repository</a>.</p> </li> </ol>
Supplementary material to "Manipulation of Robustness in Self-Organizing Systems on the the Example of UAV Defense"
<p>Supplementary material to the bachelor thesis "Manipulation of Robustness in Self-Organizing Systems on the the Example of UAV Defense".</p>
FIGURE 5 in Re-assessment of the Late Jurassic eusauropod dinosaur Hudiesaurus sinojapanorum Dong, 1997, from the Turpan Basin, China, and the evolution of hyper-robust antebrachia in sauropods
FIGURE 5. Teeth previously referred to Hudiesaurus sinojapanorum (IVPP 11121-2) but regarded as?Mamenchisauridae indet. herein. A–D, Two tooth crowns within a broken jaw element in lingual (A), labial (B), distal (C), and mesial (D) views. E–H, Isolated tooth crown in lingual (E), labial (F), distal (G), and mesial (H) views. I–L, isolated tooth crown in lingual (I), labial (J), distal (K), and mesial (L) views. Abbreviation: lb, lingual boss. Scale bars equal 10 mm.
FIGURE 1 in Re-assessment of the Late Jurassic eusauropod dinosaur Hudiesaurus sinojapanorum Dong, 1997, from the Turpan Basin, China, and the evolution of hyper-robust antebrachia in sauropods
FIGURE 1. Map showing Xinjiang Autonomous Region in China, with a magnified inset showing the approximate location of the Hudiesaurus specimens within Shanshan County.
RECIPROCALLY INHIBITORY CIRCUITS OPERATING WITH DISTINCT MECHANISMS ARE DIFFERENTLY ROBUST TO PERTURBATION AND MODULATION
<p>Contains reduced data consisting of intracellular voltage recordings from reciprocally inhibitory neurons, metadata and various annotations. This should allow you to reproduce the figures in the paper. The scripts to reproduce the figures are available at https://github.com/eomorozova/hco-analysis </p>
Preconditioners for robust optimal control problems under uncertainty - numerical tests
<p>Codes and data of the numerical experiments described in the manuscript "Preconditioners for optimal control problems under uncertainty".</p>
Robust and fast post-processing of single-shot spin qubit detection events with a neural network
<p>Dataset to the paper 'Robust and fast post-processing of single-shot spin qubit detection events with a neural network'</p>
Figure 2 from: Ito K, Anglin J, Liew S (2017) Semi-automated Robust Quantification of Lesions (SRQL) Toolbox. Research Ideas and Outcomes 3: e12259. https://doi.org/10.3897/rio.3.e12259
Figure 2 - We tested our toolbox on a mock lesion mask. A. The stroke subject's T1 anatomical scan; B. The mock lesion mask is the red sphere; the blue mask is the lesion segmentation. The white matter was intentionally covered within the mock lesion mask, but as shown here, white matter voxels are removed by the white matter correction.
Autonomous Aerial Inspection using Visual-Inertial Robust Localization and Mapping
<p>This video illustrates the content of the paper referenced below.<br> <br> <strong>Reference:</strong><br> Lucas Teixeira, Ignacio Alzugaray and Margarita Chli, "Autonomous Aerial Inspection using Visual-Inertial Robust Localization and Mapping", in Proceedings of the International Conference on Field and Service Robotics (FSR), 2017.</p> <p><strong>Abstract:</strong></p> <p>With recent technological breakthroughs bringing fully autonomous inspection using small Unmanned Aerial Vehicles (UAVs) closer to reality, the community of Robotics has actively been developing the real-time perception capabilities able to run onboard such constraint platforms. Despite good progress, realistic deployment of autonomous UAVs in GPS-denied environments is still rudimentary. In this work, we propose a novel system to generate a collision-free path towards a user-specified inspection direction for a small UAV using monocular-inertial sensing only and performing all computation onboard. Estimating both the previously unknown scene and the UAV’s trajectory on the fly, this system is evaluated on real experiments outdoors in the presence of wind and poorly structured environments. Our analysis reveals the shortcomings of using sparse feature maps for planning, highlighting the importance of robust dense scene estimation proposed here.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.